{"id":"https://openalex.org/W2026581312","doi":"https://doi.org/10.1109/cvpr.2012.6247757","title":"Weakly supervised structured output learning for semantic segmentation","display_name":"Weakly supervised structured output learning for semantic segmentation","publication_year":2012,"publication_date":"2012-06-01","ids":{"openalex":"https://openalex.org/W2026581312","doi":"https://doi.org/10.1109/cvpr.2012.6247757","mag":"2026581312"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2012.6247757","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2012.6247757","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE Conference on Computer Vision and Pattern Recognition","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5019603588","display_name":"Alexander Vezhnevets","orcid":null},"institutions":[{"id":"https://openalex.org/I35440088","display_name":"ETH Zurich","ror":"https://ror.org/05a28rw58","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I35440088"]}],"countries":["CH"],"is_corresponding":true,"raw_author_name":"A. Vezhnevets","raw_affiliation_strings":["ETH Zurich, Zurich, Switzerland","ETH Z\u00fcrich, Z\u00fcrich, Switzerland"],"affiliations":[{"raw_affiliation_string":"ETH Zurich, Zurich, Switzerland","institution_ids":["https://openalex.org/I35440088"]},{"raw_affiliation_string":"ETH Z\u00fcrich, Z\u00fcrich, Switzerland","institution_ids":["https://openalex.org/I35440088"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101423778","display_name":"Vittorio Ferrari","orcid":"https://orcid.org/0000-0002-1942-233X"},"institutions":[{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"V. Ferrari","raw_affiliation_strings":["University of Edinburgh, Edinburgh, UK","\u00a7The University of Edinburgh, Edinburgh, UK"],"affiliations":[{"raw_affiliation_string":"University of Edinburgh, Edinburgh, UK","institution_ids":["https://openalex.org/I98677209"]},{"raw_affiliation_string":"\u00a7The University of Edinburgh, Edinburgh, UK","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038199211","display_name":"Joachim M. Buhmann","orcid":"https://orcid.org/0000-0002-6613-7101"},"institutions":[{"id":"https://openalex.org/I35440088","display_name":"ETH Zurich","ror":"https://ror.org/05a28rw58","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I35440088"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"J. M. Buhmann","raw_affiliation_strings":["ETH Zurich, Zurich, Switzerland","ETH Z\u00fcrich, Z\u00fcrich, Switzerland"],"affiliations":[{"raw_affiliation_string":"ETH Zurich, Zurich, Switzerland","institution_ids":["https://openalex.org/I35440088"]},{"raw_affiliation_string":"ETH Z\u00fcrich, Z\u00fcrich, Switzerland","institution_ids":["https://openalex.org/I35440088"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5019603588"],"corresponding_institution_ids":["https://openalex.org/I35440088"],"apc_list":null,"apc_paid":null,"fwci":16.1984,"has_fulltext":false,"cited_by_count":157,"citation_normalized_percentile":{"value":0.99337238,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":100},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7586649656295776},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6895212531089783},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5724494457244873},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5627766251564026},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.5223968029022217},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4783318042755127},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.14621329307556152}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7586649656295776},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6895212531089783},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5724494457244873},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5627766251564026},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.5223968029022217},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4783318042755127},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.14621329307556152}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/cvpr.2012.6247757","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2012.6247757","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE Conference on Computer Vision and Pattern Recognition","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.ed.ac.uk:publications/29e13b0e-a74e-44ee-a0b3-f5ce13e848ad","is_oa":false,"landing_page_url":"https://www.research.ed.ac.uk/en/publications/29e13b0e-a74e-44ee-a0b3-f5ce13e848ad","pdf_url":null,"source":{"id":"https://openalex.org/S4406922455","display_name":"Edinburgh Research Explorer","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":""}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1528789833","https://openalex.org/W1542723449","https://openalex.org/W1746819321","https://openalex.org/W1748750709","https://openalex.org/W1966949944","https://openalex.org/W1999478155","https://openalex.org/W2029731618","https://openalex.org/W2060280062","https://openalex.org/W2100588357","https://openalex.org/W2116219421","https://openalex.org/W2116877738","https://openalex.org/W2123023145","https://openalex.org/W2124386111","https://openalex.org/W2125849446","https://openalex.org/W2143516773","https://openalex.org/W2148596671","https://openalex.org/W2158427031","https://openalex.org/W2166566250","https://openalex.org/W2169003314","https://openalex.org/W2169551590","https://openalex.org/W2172156083","https://openalex.org/W2429914308","https://openalex.org/W2535516436","https://openalex.org/W2536305071","https://openalex.org/W2538008885","https://openalex.org/W2911964244","https://openalex.org/W2951665052","https://openalex.org/W3124229194","https://openalex.org/W3147929628","https://openalex.org/W4211049957","https://openalex.org/W4239874992","https://openalex.org/W4252621450","https://openalex.org/W6631412525","https://openalex.org/W6632547051","https://openalex.org/W6637730771","https://openalex.org/W6678684981","https://openalex.org/W6684935187","https://openalex.org/W6764988152","https://openalex.org/W6981926866"],"related_works":["https://openalex.org/W3162567751","https://openalex.org/W4285260836","https://openalex.org/W4221088574","https://openalex.org/W3094076422","https://openalex.org/W3046775127","https://openalex.org/W4220686584","https://openalex.org/W4319309271","https://openalex.org/W2971361125","https://openalex.org/W3091943846","https://openalex.org/W2953328427"],"abstract_inverted_index":{"We":[0,85,103,146],"address":[1],"the":[2,16,25,31,73,114,120,129,205],"problem":[3,153],"of":[4,76,90,116,190],"weakly":[5,55,81,214],"supervised":[6,56,65,82,215,222],"semantic":[7],"segmentation.":[8],"The":[9],"training":[10,83,197],"images":[11,29],"are":[12,194],"labeled":[13],"only":[14],"by":[15,21,63],"classes":[17,192],"they":[18],"contain,":[19],"not":[20],"their":[22],"location":[23],"in":[24,53,99],"image.":[26],"On":[27],"test":[28],"instead,":[30],"method":[32],"must":[33],"predict":[34],"a":[35,87,100,105,117,133,150,181,209],"class":[36],"label":[37],"for":[38,128],"every":[39],"pixel.":[40],"Our":[41,166],"goal":[42],"is":[43,61,69,132,169],"to":[44,48,59,71,162],"enable":[45],"segmentation":[46],"algorithms":[47],"use":[49],"multiple":[50,143],"visual":[51,78,97,191],"cues":[52,79,98],"this":[54],"setting,":[57],"analogous":[58],"what":[60],"achieved":[62],"fully":[64,221],"methods.":[66,223],"However,":[67],"it":[68,148],"difficult":[70],"assess":[72],"relative":[74],"usefulness":[75],"different":[77,101],"from":[80,119],"data.":[84],"define":[86],"parametric":[88],"family":[89,121],"structured":[91],"models,":[92],"were":[93],"each":[94],"model":[95,109,118,131],"weights":[96],"way.":[102],"propose":[104,155],"Maximum":[106],"Expected":[107],"Agreement":[108],"selection":[110],"principle":[111],"that":[112,175,193],"evaluates":[113],"quality":[115],"without":[122],"looking":[123],"at":[124,196],"superpixel":[125,178],"labels.":[126],"Searching":[127],"best":[130],"hard":[134],"optimization":[135,152],"problem,":[136],"which":[137],"has":[138],"no":[139],"analytic":[140],"gradient":[141],"and":[142,154,198,200,217],"local":[144],"optima.":[145],"cast":[147],"as":[149,180],"Bayesian":[151],"an":[156,170],"algorithm":[157],"based":[158],"on":[159,204],"Gaussian":[160],"processes":[161],"efficiently":[163],"solve":[164],"it.":[165],"second":[167],"contribution":[168],"Extremely":[171],"Randomized":[172],"Hashing":[173],"Forest":[174],"represents":[176],"diverse":[177],"features":[179],"sparse":[182],"binary":[183],"vector.":[184],"It":[185],"enables":[186],"using":[187],"appearance":[188],"models":[189],"fast":[195],"testing":[199],"yet":[201],"accurate.":[202],"Experiments":[203],"SIFT-flow":[206],"dataset":[207],"show":[208],"significant":[210],"improvement":[211],"over":[212,219],"previous":[213],"methods":[216],"even":[218],"some":[220]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":12},{"year":2019,"cited_by_count":18},{"year":2018,"cited_by_count":12},{"year":2017,"cited_by_count":17},{"year":2016,"cited_by_count":22},{"year":2015,"cited_by_count":28},{"year":2014,"cited_by_count":19},{"year":2013,"cited_by_count":12}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
